code: def test_square_hinge(backend_default):
      outputs = np.array([0.3, 0.7]).reshape((2, 1))
      targets = np.array([0, 1]).reshape((2, 1))
      shifted_targets = np.array([-1, 1]).reshape((2, 1))
      expected_result = np.mean(
          np.square(np.maximum(1 - shifted_targets * outputs, 0)),
          axis=0,
      )
      compare_tensors(
          SquareHingeLoss(), outputs, targets, expected_result, tol=1e-7
      )
  
imports: import itertools
  import numpy
  from neon import NervanaObject
  from neon.backends import gen_backend
  from neon.transforms import CrossEntropyBinary, CrossEntropyMulti, SumSquared, MeanSquared, Misclassification, PrecisionRecall, SmoothL1Loss, SquareHingeLoss
label_desc: Use image node_id 23 for calling a global function with example usage: test_square_hinge(backend_default) without return types